A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization

Abstract

The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision underwater navigation and positioning. To solve the problem, a fusion scheme based on factor graph optimization (FGO) is proposed. Due to multiple iterations and joint optimization of historical data, FGO could usually show a better performance than the traditional Kalman filter. In addition, considering that USBL and DVL are usually heavily influenced by the environment, outliers are often present. A robust integrated navigation algorithm based on a maximum correntropy criterion and FGO scheme is proposed. The proposed algorithm solves the problem of multi-sensor fusion and non-Gaussian noise. Numerical simulations and field tests demonstrate that the proposed FGO scheme shows a better performance and robustness than the traditional Kalman filter. Compared with the traditional Kalman filtering, the positioning accuracy is improved by 5.3%, 9.1%, and 5.1% in the east, north, and height directions. It can realize a more accurate navigation and positioning of underwater multi-sensors.

A Low-Cost and High-Precision Underwater Integrated Navigation System

“…For the search mission for underwater targets, the collaborative operation mode of USV range coarse scanning and AUV approach reconnaissance has the advantages of a high search efficiency, flexible deployment and real-time information transmission. The underwater environment has characteristics such as strong closure and complexity, so the underwater navigation system is the key to AUVs executing underwater navigation tasks [5][6][7]. The AUV navigation method can be subdivided into five main branches: dead reckoning, vision-aided navigation, sonar-aided navigation, map matching navigation and acoustic navigation.…”

Sensor Fusion for Underwater Vehicle Navigation Compensating Misalignment Using Lie Theory

“…The underwater vehicles installed with sensors for environmental monitoring can navigate through sites of interest and accumulate the sensor data for analysis. The inertial navigation system (INS) and Doppler velocity log (DVL) play pivotal roles in ensuring accurate underwater navigation [ 1 , 2 , 3 , 4 , 5 ]. Misalignment between the INS and DVL results in a degradation of overall navigation performance.…”

“…A new approach that combines factor graph optimization (FGO) and the maximum entropy criterion is proposed. This method replaces conventional nonlinear filters, whose performance degrades in the case of non-Gaussian measurement noise [ 2 ]. It also incorporates USBL into INS/DVL navigation.…”

Research and implementation of underwater SINS/DVL integrated navigation and positioning algorithm

“…DVL measures the velocity information of the underwater vehicle relative to the bottom or water layer according to the Doppler effect [2], and its error does not accumulate over time. The application conditions of underwater acoustic positioning systems based on arrays are complex [3][4]. Therefore, based on the advantages of SINS/DVL integrated navigation positioning and navigation system, such as easy integration and development, and strong autonomy [5], this paper carries out the application research of this navigation system.…”